QoS based deliver model of the packages transportation by the passenger train and solution algorithm

In the location science, environmental effect becomes a new main consideration for site selection. For the unwanted facility location selection, decision makers should consider the cost of resolving the environmental conflict. We introduced the negative influence cost for the facility which was inversely proportional to distance between the facility and residents. An unwanted facility location problem was suggested to minimize the sum of the negative influence cost and the transportation cost. The objective cost function was analyzed as nonlinear type and was neither convex nor concave. Three GRASP (Greedy Randomized adaptive Search Procedure) methods as like Random_GRASP, Epsilon_GRASP and GRID_GRASP were developed to solve the unwanted facility location problem. The Newton's method for nonlinear optimization problem was used for local search in GRASP. Experimental results showed that quality of solution of the GRID_GRASP was better than those of Random_GRASP and Epsilon_GRASP. The calculation time of Random_GRASP and Epsilon_GRASP were faster than that of Grid_GRASP.

This paper presents a greedy randomized adaptive search procedure (GRASP) for scheduling n jobs on m nonhomogeneous parallel machines with time windows. An additional feature of the problem is that each job falls into one of ρ priority classes. The objective is to maximize the number of jobs scheduled, where a job in a higher priority class has infinitely more value than a job in a lower priority class. The GRASP consists of two phases. The first phase produces feasible solutions by ranking each job with a greedy function and then selecting one at random from a restricted candidate list. The process is repeated until no more jobs can be scheduled. The second phase seeks a local optimum by searching over a series of neighborhoods defined by job insertions and exchanges. The algorithm is compared to a dynamic-programming heuristic that sequentially schedules the jobs in each priority class. Extensive computational results are presented based on data drawn from an application involving the use of communications relay satellites.

A GRASP algorithm for the concrete delivery problem

Recent railway industry campaigns have highlighted the relative average fuel efficiency of freight and passenger trains as a key benefit of the railway transportation mode. These efficiencies are anticipated to increase rail market share as rising energy costs make less efficient competing modes less attractive. However, the fuel consumption and energy efficiency of a specific passenger or freight rail system, and even individual trains, depend on many factors. Changes in these factors can have various effects on the overall fuel consumption and efficiency of the system. One of these factors is the amount of congestion and delay due to increased traffic on the line. Thus, it is possible that the additional traffic anticipated to shift to the rail mode due to its energy benefits may increase congestion and actually have a negative impact on overall network energy efficiency. Such a case would tend to dampen the future shift of traffic to the rail mode. While simple train performance calculators can evaluate the energy efficiency of a train for an ideal run, more powerful train dispatching simulation software is required to simulate the performance of trains in realistic operating scenarios on congested single-track lines. Using this software, the relative impact of congestion on efficiency can be analyzed and compared to changes in factors related to fuel consumption. In this study, several factors affecting the efficiency of both passenger and freight rail systems were selected for analysis. Rail Traffic Controller (RTC), a train dispatching software, simulated representative single-track rail subdivisions to determine the performance of specific passenger and freight trains under different combinations of factor level settings. For passenger rail, the effects of traffic volume and station spacing on fuel consumption were analyzed while the effects of traffic volume and average speed were analyzed for freight rail. Each system was analyzed on level track and on territory with grades. Preliminary results suggest that passenger trains, if given priority, maintain their efficiency until large numbers of passenger trains are present on the network, while freight trains experience degradation in energy efficiency as congestion increases. These results will be used to develop a factorial experiment to evaluate the relative sensitivity of freight and passenger rail efficiency to congestion and other system parameters. The paper concludes with a brief discussion of possible technologies to improve efficiency and offset potential losses due to future congestion.

Train Operators can improve railway passengers’ service quality and traffic management by accurately predicting travel arrangements and delays. Precise prediction of train delays is vital for creating feasible scheduled timetables. The import of pruning stacked ensemble deep neural networks into train delay prediction helps improve model prediction accuracy and computational time. In this study, we propose a novel pruning stacked ensemble learning model that uses pruned multilayer perceptron (MLP) neural networks as a meta-learner and heterogeneous neural networks as sub-models to improve the prediction accuracy for passenger train arrival delays and evaluate the model performance using Amtrak, the most extensive US passenger railroad service data, to determine the enhanced model accuracy. We used stacked generalization regression neural networks for artificial neural networks and deep learning neural networks to create the stacked ensemble models. We optimized the models using OPTUNA and produced a pruning stacked ensemble model using the best neural network sub-models and an MLP meta-learner. Our methodology comprises data preprocessing, feature engineering, modeling, case studies, and evaluation phases. Our experiments demonstrate that our proposed pruning stacked ensemble multilayer perceptron Neural Network (PST-NN) model has various degrees of improvement in model evaluation indicators compared to the existing model by 35.20%, 53.40% in terms of accuracy and prediction error and outperforms the benchmark models by 85.22% in terms prediction error in Passenger train delay prediction respectively, which provides a new approach to building models efficiently in Passenger train arrival delay prediction.

BackgroundTrain is a common mode of public transport across the globe; however, the risk of COVID-19 transmission among individual train passengers remains unclear.MethodsWe quantified the transmission risk of COVID-19 on high-speed train passengers using data from 2,334 index patients and 72,093 close contacts who had co-travel times of 0–8 hours from 19 December 2019 through 6 March 2020 in China. We analysed the spatial and temporal distribution of COVID-19 transmission among train passengers to elucidate the associations between infection, spatial distance, and co-travel time.ResultsThe attack rate in train passengers on seats within a distance of 3 rows and 5 columns of the index patient varied from 0 to 10.3% (95% confidence interval [CI] 5.3% – 19.0%), with a mean of 0.32% (95%CI 0.29% – 0.37%). Passengers in seats on the same row as the index patient had an average attack rate of 1.5% (95%CI 1.3% – 1.8%), higher than that in other rows (0.14%, 95%CI 0.11% – 0.17%), with a relative risk (RR) of 11.2 (95%CI 8.6 –14.6). Travellers adjacent to the index patient had the highest attack rate (3.5%, 95%CI 2.9% – 4.3%) of COVID-19 infections (RR 18.0, 95%CI 13.9 – 23.4) among all seats. The attack rate decreased with increasing distance, but it increased with increasing co-travel time. The attack rate increased on average by 0.15% (p = 0.005) per hour of co-travel; for passengers at adjacent seats, this increase was 1.3% (p = 0.008), the highest among all seats considered.ConclusionsCOVID-19 has a high transmission risk among train passengers, but this risk shows significant differences with co-travel time and seat location. During disease outbreaks, when travelling on public transportation in confined spaces such as trains, measures should be taken to reduce the risk of transmission, including increasing seat distance, reducing passenger density, and use of personal hygiene protection.

Adaptive subsequence adjustment with evolutionary asymmetric path-relinking for TDRSS scheduling

Web service discovery (WSD) is the task of matchmaking a set of relevant web services. Quality of service (QoS) has recently been applied to represent non-functional properties of web services. Therefore, when those services provide the same functionality but have different QoS values, how to effectively filter out the services that cannot satisfy the QoS constraints and rank the remaining services is still an open research issue. In this paper, we propose an integrated approach that allows a service requester to specify a functionality request, multiple QoS constraints and their preferences, and our method discovers a set of the most appropriate ranked services with QoS utility aggregation. By conducting empirical experiments on simulated web services, we validate the feasibility of our service discovery approach. The running example shows that, our approach can find more appropriate services by the satisfiability of multiple QoS constraints and the ranking of aggregated QoS.

Web service composition (WSC) is the task of combining a chain of connected single services together to create a more complex and value-added composite service. Quality of service (QoS) has been mostly applied to represent nonfunctional properties of web services and differentiate those with the same functionality. Many research has been done on QoS-aware service composition, as it significantly affects the quality of a composite service. However, existing methods are restricted to predefined workflows, which can incur a couple of limitations, including the lack of guarantee for the optimality on overall QoS and for the completeness of finding a composite service solution. In this paper, instead of predefining a workflow model for service composition, we propose a novel planning-based approach that can automatically convert a QoS-aware composition task to a planning problem with temporal and numerical features. Furthermore, we use state-of-the-art planners, including an existing one and a self-developed one, to handle complex temporal planning problems with logical reasoning and numerical optimization. Our approach can find a composite service graph with the optimal overall QoS value while satisfying multiple global QoS constraints. We implement a prototype system and conduct extensive experiments on large web service repositories. The experimental results show that our proposed approach largely outperforms existing ones in terms of solution quality and is efficient enough for practical deployment.

The aim of the research described in this paper has been to construct a model to enable an analysis to be made of the quality of service in both high speed rail (HSR) and air transport (AT) networks and, on this basis, establish conditions for competition between HSR and AT systems. The model is based on the known physical characteristics of both networks, represented by the number of nodes, the number of links, strategies for linking individual nodes; the system characteristics of the two modes of transport—the set of high speed trains and aircraft used (numbers of seats and unit operating costs); the characteristics of the demand between individual nodes on the network in a given interval of time; and the costs of transport in each system. The chief outputs of the model are: total passenger travel costs between origins and destinations, using HSR and AT, and the minimum total costs of the systems. Both types of costs are used as criteria to establish the conditions for competition between high speed raid (HSR) and air transport (AT).

Mobile ad Hoc Networks (MANETs) are one of the most important technologies supporting Ubiquitous and Pervasive Computing (UPC). As many UPC applications pose Quality of Service (QoS) constraints, their implementation in MANETs becomes dependent on the MANET algorithms for QoS routing. In this paper Genetic Algorithms (GAs) for QoS routing in MANETs are considered. GAs can solve the NP search of QoS routes with multiple constraints, and then address the UPC QoS requirements. The focus is on tree-based GAs, which represent the set of paths from source to destination as a tree and encode them through the crossed junctions. They encode single paths in the chromosome. We investigate on the effects of binary encoding schema on tree-based GAs. To this purpose we design a GA with binary encoding that maps classes of paths in single chromosomes. These classes are both collectively exhaustive and mutually exclusive. The GA with binary encoding uses an adaptive mutation probability for deeper exploration of the search space, and local search on classes of paths. Simulation results compare the GA with binary encoding with two applications of GAMAN, the main existing tree-based GA. They show that the binary encoding allows the GA to converge faster although it introduces additional computational costs.

Multi-constrained Quality of Service (QoS) routing is to find a feasible path that satisfies multiple constraints simultaneously, which is a big challenge for Mobile Ad hoc Networks (MANETs) where the topology may change constantly. Most of the conventional protocols are designed to concentrate on either maximizing the throughput or decreasing the end-to-end delay. These single constrained QoS routing protocols have inherent shortcomings in real time applications where the communication requires meeting stringent requirements on delay, delay-jitter, cost and other QoS metrics. In this paper we propose an agent based Multi-Constrained QoS aware multicast routing scheme based on MAODV (MC_MAODV) which uses a set of static and mobile agent. It depicts QoS multicast model with multiple constraints which may deal with bandwidth reservation, delay constraint and packet loss to multicast session. Here the mobile agents are used to find multicast routes, create the backbone for a reliable multicasting and to adapt to dynamic topology. Multi-path strategy is introduced to reduce the time consumption of rerouting when the link fails due to node mobility. Also, the source node can utilize the best path for the data transmission to meet multiple QoS requirements. Extensive simulations have been conducted to evaluate the performance of MC_MAODV using Network Simulator (NS-2). The simulation results show that the proposed scheme performs better than traditional MAODV in terms of improving packet delivery ratio and minimizing end-to-end delay.KeywordsMANETsmulti-constrained routing multicastingclustersend-to-end delaypacket loss ratio

Multimodal transportation plays a vital role in the efficiency of social economic, the path and transport mode selection in multimodal transportation contribute to the benefit of carriers and customers directly, on the basis of analyzing the transportation cost and time cost of multimodal transportation, a multi-objective model is proposed. It works on minimization of transit and transfer costs, and in order to meet the customer's demand in time, a punishment is set up. According to the characteristics of the model, a Floyd-GA algorithm is designed, through which the relative shortest path is found and the transport mode is distributed for each route. Finally, comparing the results with the traditional method, the effectiveness of the above algorithms is testified.

With the growing demand of multimedia applications, quality of service (QoS) assured that multicast routing is an important issue in wireless mesh networks. Finding a multicast tree which satisfies the multiple constraints is a NP-Complete problem. In this paper, we propose a discrete particle swarm optimization (DPSO) approach for finding the minimum tree cost from a given source to a set of destination with delay and bandwidth constraint. The concept of relative position base indexing (RPI) is used to convert a continuous space to discrete space for multicast routing. The simulation is carried out in NS-2.26 and the comparison is made with respect to tree cost under two scenarios, namely varying node mobility and increasing number of nodes. The results demonstrate that DPSO has better speed, performance, and efficiency than multicast routing based on genetic algorithm.

The depopulation of the countryside by the industrial revolution, which so troubled the English, did not take place in the United States, although the character of the population changed dramatically. A growing tendency for farm boys (and more and more girls) to forsake the land for the rising urban centers was apparent by the turn of the century, and it alarmed a few old-timers like James J. Hill, who saw in it a fatuous belief on the part of young people that they could “live without working.” But most Americans were proud of the transformation of villages into towns and towns into ambitious cities—a trend that would last as long as the railroad train was the chief mode of intercity transportation. Most people saw in such trends nothing less than the march of progress across what had been, within the memory of many still living in 1920, an empty, inviting landscape. The passenger train, which so many historians have seen as “knitting together” localities into a national whole, did no such thing. It rather created those local and regional centers, in the process of creating a highly vertebrate nation that by the early twentieth century had achieved an efficiency and trustworthiness of travel, transport, and communication that would not survive the emergence of the automobile and its suburbs. Reflecting upon the fact that it was the railroads that created the heavy industry that made universal ownership of cars possible, we can see how the passenger train contained the seeds of its own destruction. In its day, however, which lasted almost a century, the train was as totally in command of national mobility as the car has been since.